In this script, there is conducted the estimation for the measure_marginal approach for a single given env. The programs is the set of bytecode programs with varying number of opcodes. The measurements are time measurements of program runs or benchmarks. The estimation of each opcode is calculated as the regression against the number of a given opcode in the executed programs.

Parametrization. The evm client name env=geth, the file with programs programs=pg_marginal_full_step5_v2.csv, the file with measurements results=results_marginal_full_geth.csv,
the output csv file with estimated cost output_estimated_cost=reports-08.11.2024/estimated_cost_geth_full.csv, should the details be included in the report details=TRUE.

Data preparation. Reading data from the programs file and results file. Initial adjustments.

# The example of programs file: pg_marginal_full5_c50_step1_shuffle.csv .
# The example of results file: geth_pg_marginal_full5_c50_step1_shuffle_50_4.csv
programs = read.csv(params$programs)
results = load_data_set_from_file(params$results)
if(!("run_id" %in% colnames(results))) {
  results$run_id <- 1
}
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "total_time_ns")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$total_time_ns != 0), ]

if (class(results[,"total_time_ns"]) == "character") {
  stop("at least one  of 'total_time_ns' value cannot be parsed into numeric type")
}
measurements = sqldf(paste0("SELECT opcode, op_count, sample_id, run_id, total_time_ns as measure_total_time_ns, '", env, "' as env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)"))
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
# head(measurements)

extract_opcodes <- function() {
  unique(measurements$opcode)
}

all_opcodes = extract_opcodes()

Measurement point distribution. For bare eye assessment. Every point is a sinle measurement. For each opcode and op_count, the measurements should tend to be concentrated around a single value.

for (opcode in all_opcodes) {
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  plot(measure_total_time_ns ~ op_count, data=df, las=2)
  title(main=paste(env, opcode, "- measurement point distribution"))
}

The comparision of result. Before and after removing outlying measurement. Switch removed_outliers to FALSE to see the comparison.

if (removed_outliers) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', c(env))
}

# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, use_median) {
  if (missing(plots)) {
    plots = "scatter"
  }
  if (missing(use_median)) {
    use_median = FALSE
  }
  if (plots == "all") {
    print(c(opcode, env))
  }
  
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  
  if (use_median) {
    f = median
  } else {
    f = mean
  }
  df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
  step_=max(df_mean$op_count)/(nrow(df_mean)-1)

  model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
  model_mean_summary = summary(model_mean)
  if (plots == "diagnostics" | plots == "all") {
    print(model_mean_summary)
  }
  slope = model_mean_summary$coefficients['op_count','Estimate']
  intercept = model_mean_summary$coefficients[1,'Estimate']
  stderr = model_mean_summary$coefficients['op_count','Std. Error']
  
  if (plots == "scatter" | plots == "all") {
    par(mfrow=c(1,1))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    rounded_slope = round(slope, 3)
    rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
    rounded_stderr = round(stderr, 3)
    title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
    abline(a=intercept-slope*step_, b=slope*step_, col="red")
  }
  if (plots == "diagnostics" | plots == "all") {
    par(mfrow=c(2,2))
    plot(model_mean)
  }
  list("slope" = slope, "stderr" = stderr)
}
# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')

Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not be an issue.

for (opcode in all_opcodes) {
  boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}

Now we can investigate the linear regressions.

for (opcode in all_opcodes) {
  estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, plots=ifelse(details,'all','scatter'))
  estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}
## [1] "ADD"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -333.77 -133.09   49.32  104.31  367.41 
## 
## Coefficients:
##             Estimate Std. Error t value          Pr(>|t|)    
## (Intercept) 8064.614    118.501   68.06 0.000000000000161 ***
## op_count     -51.441      4.006  -12.84 0.000000431613844 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 210.1 on 9 degrees of freedom
## Multiple R-squared:  0.9482, Adjusted R-squared:  0.9425 
## F-statistic: 164.9 on 1 and 9 DF,  p-value: 0.0000004316

## [1] "MUL"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.386 -34.011   5.795  32.000  70.136 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4975.1591    29.2904  169.86 < 0.0000000000000002 ***
## op_count      13.0045     0.9902   13.13          0.000000356 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51.93 on 9 degrees of freedom
## Multiple R-squared:  0.9504, Adjusted R-squared:  0.9449 
## F-statistic: 172.5 on 1 and 9 DF,  p-value: 0.0000003557

## [1] "SUB"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -148.52  -48.53  -38.41   33.23  173.03 
## 
## Coefficients:
##             Estimate Std. Error t value           Pr(>|t|)    
## (Intercept) 5136.523     59.268  86.667 0.0000000000000184 ***
## op_count       5.245      2.004   2.618             0.0279 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 105.1 on 9 degrees of freedom
## Multiple R-squared:  0.4322, Adjusted R-squared:  0.3691 
## F-statistic: 6.852 on 1 and 9 DF,  p-value: 0.02792

## [1] "DIV"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.2500  -6.7864   0.6545   5.3250  17.7182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4987.2500     5.4358  917.48 < 0.0000000000000002 ***
## op_count       7.8064     0.1838   42.48      0.0000000000111 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.637 on 9 degrees of freedom
## Multiple R-squared:  0.995,  Adjusted R-squared:  0.9945 
## F-statistic:  1805 on 1 and 9 DF,  p-value: 0.00000000001108

## [1] "SDIV" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.1364  -2.6864   0.2636   5.7136   7.9636 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4989.1364     4.6016 1084.21 < 0.0000000000000002 ***
## op_count       9.5800     0.1556   61.58    0.000000000000396 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.158 on 9 degrees of freedom
## Multiple R-squared:  0.9976, Adjusted R-squared:  0.9974 
## F-statistic:  3792 on 1 and 9 DF,  p-value: 0.0000000000003958

## [1] "MOD"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.091  -5.032   2.582   3.750  21.855 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4986.0909     7.3166  681.47 < 0.0000000000000002 ***
## op_count       7.4109     0.2473   29.96       0.000000000251 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.97 on 9 degrees of freedom
## Multiple R-squared:  0.9901, Adjusted R-squared:  0.989 
## F-statistic: 897.7 on 1 and 9 DF,  p-value: 0.0000000002512

## [1] "SMOD" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.568  -5.321   2.468   5.334   7.623 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4978.5682     4.1857 1189.42 < 0.0000000000000002 ***
## op_count       9.3155     0.1415   65.83    0.000000000000217 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.42 on 9 degrees of freedom
## Multiple R-squared:  0.9979, Adjusted R-squared:  0.9977 
## F-statistic:  4334 on 1 and 9 DF,  p-value: 0.0000000000002174

## [1] "ADDMOD" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.500  -7.436   2.427   7.600  20.136 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4988.0000     7.7067  647.23 < 0.0000000000000002 ***
## op_count      12.6145     0.2605   48.42     0.00000000000343 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.66 on 9 degrees of freedom
## Multiple R-squared:  0.9962, Adjusted R-squared:  0.9958 
## F-statistic:  2344 on 1 and 9 DF,  p-value: 0.000000000003428

## [1] "MULMOD" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.932  -2.618   3.805   9.041  12.936 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4982.9318     8.2442  604.42 < 0.0000000000000002 ***
## op_count      22.4264     0.2787   80.47   0.0000000000000358 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.62 on 9 degrees of freedom
## Multiple R-squared:  0.9986, Adjusted R-squared:  0.9985 
## F-statistic:  6475 on 1 and 9 DF,  p-value: 0.0000000000000358

## [1] "EXP"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.273  -5.691   2.491  13.364  19.236 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 5005.2727    11.4545  436.97 < 0.0000000000000002 ***
## op_count      26.9745     0.3872   69.66    0.000000000000131 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.31 on 9 degrees of freedom
## Multiple R-squared:  0.9981, Adjusted R-squared:  0.9979 
## F-statistic:  4852 on 1 and 9 DF,  p-value: 0.0000000000001308

## [1] "SIGNEXTEND" "geth"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.9091  -6.8364   0.1636   4.2273  20.1455 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4971.727      5.708  871.00 < 0.0000000000000002 ***
## op_count       9.004      0.193   46.66     0.00000000000478 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.12 on 9 degrees of freedom
## Multiple R-squared:  0.9959, Adjusted R-squared:  0.9954 
## F-statistic:  2177 on 1 and 9 DF,  p-value: 0.000000000004777

## [1] "LT"   "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.523  -4.875   2.796   5.216  12.864 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4981.5227     5.5586  896.17 < 0.0000000000000002 ***
## op_count       7.3227     0.1879   38.97       0.000000000024 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.854 on 9 degrees of freedom
## Multiple R-squared:  0.9941, Adjusted R-squared:  0.9935 
## F-statistic:  1519 on 1 and 9 DF,  p-value: 0.00000000002399

## [1] "GT"   "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.0227  -2.7977  -0.9227   5.4773  13.7273 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4977.023      5.947  836.88 < 0.0000000000000002 ***
## op_count       7.390      0.201   36.76      0.0000000000405 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.54 on 9 degrees of freedom
## Multiple R-squared:  0.9934, Adjusted R-squared:  0.9926 
## F-statistic:  1351 on 1 and 9 DF,  p-value: 0.00000000004046

## [1] "SLT"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -104.773  -29.545   -3.318   19.314  132.473 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4952.318     34.867 142.033 < 0.0000000000000002 ***
## op_count      10.649      1.179   9.034           0.00000828 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 61.81 on 9 degrees of freedom
## Multiple R-squared:  0.9007, Adjusted R-squared:  0.8896 
## F-statistic: 81.62 on 1 and 9 DF,  p-value: 0.000008276

## [1] "SGT"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.296  -4.214   2.527   6.479  20.673 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4992.7955     7.6102  656.07 < 0.0000000000000002 ***
## op_count       8.0355     0.2573   31.23       0.000000000173 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.49 on 9 degrees of freedom
## Multiple R-squared:  0.9909, Adjusted R-squared:  0.9898 
## F-statistic: 975.5 on 1 and 9 DF,  p-value: 0.0000000001734

## [1] "EQ"   "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.409  -3.182  -2.073   8.318  18.536 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4980.4091     7.3168  680.68 < 0.0000000000000002 ***
## op_count       7.0109     0.2474   28.34       0.000000000412 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.97 on 9 degrees of freedom
## Multiple R-squared:  0.9889, Adjusted R-squared:  0.9877 
## F-statistic: 803.4 on 1 and 9 DF,  p-value: 0.0000000004121

## [1] "ISZERO" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.591  -7.318  -5.909  -2.068  55.682 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4981.5909    11.5699  430.56 < 0.0000000000000002 ***
## op_count       4.5091     0.3911   11.53           0.00000108 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.51 on 9 degrees of freedom
## Multiple R-squared:  0.9366, Adjusted R-squared:  0.9295 
## F-statistic: 132.9 on 1 and 9 DF,  p-value: 0.000001083

## [1] "AND"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.477  -3.668   1.314   7.500   9.418 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4983.4773     5.9110  843.09 < 0.0000000000000002 ***
## op_count       7.0209     0.1998   35.13      0.0000000000606 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.48 on 9 degrees of freedom
## Multiple R-squared:  0.9928, Adjusted R-squared:  0.992 
## F-statistic:  1234 on 1 and 9 DF,  p-value: 0.00000000006058

## [1] "OR"   "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.0455  -4.4773   0.6364   6.2955  12.8182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4974.0455     5.4688  909.54 < 0.0000000000000002 ***
## op_count       7.0091     0.1849   37.91      0.0000000000307 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.695 on 9 degrees of freedom
## Multiple R-squared:  0.9938, Adjusted R-squared:  0.9931 
## F-statistic:  1437 on 1 and 9 DF,  p-value: 0.00000000003068

## [1] "XOR"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.1591  -7.3841  -0.2364   7.3659  17.9818 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4980.1591     6.7769  734.88 < 0.0000000000000002 ***
## op_count       6.9718     0.2291   30.43       0.000000000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.01 on 9 degrees of freedom
## Multiple R-squared:  0.9904, Adjusted R-squared:  0.9893 
## F-statistic: 926.1 on 1 and 9 DF,  p-value: 0.0000000002187

## [1] "NOT"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.0455 -2.0455 -0.5455  1.2045  9.9545 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4967.0454     2.8249 1758.29 < 0.0000000000000002 ***
## op_count       5.0000     0.0955   52.36      0.0000000000017 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.008 on 9 degrees of freedom
## Multiple R-squared:  0.9967, Adjusted R-squared:  0.9964 
## F-statistic:  2741 on 1 and 9 DF,  p-value: 0.0000000000017

## [1] "BYTE" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.0455  -6.9545  -2.6818   0.9091  25.4091 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4971.1364     7.2135  689.15 < 0.0000000000000002 ***
## op_count       8.2545     0.2439   33.85      0.0000000000845 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.79 on 9 degrees of freedom
## Multiple R-squared:  0.9922, Adjusted R-squared:  0.9913 
## F-statistic:  1146 on 1 and 9 DF,  p-value: 0.00000000008453

## [1] "SHL"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -46.205  -5.818   2.818  14.159  19.023 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 5016.2045    11.2889  444.35 < 0.0000000000000002 ***
## op_count       7.9136     0.3816   20.74         0.0000000066 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.01 on 9 degrees of freedom
## Multiple R-squared:  0.9795, Adjusted R-squared:  0.9772 
## F-statistic:   430 on 1 and 9 DF,  p-value: 0.000000006599

## [1] "SHR"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.273  -3.782   4.255   5.768   9.018 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4981.7727     5.6987  874.20 < 0.0000000000000002 ***
## op_count       8.2473     0.1927   42.81      0.0000000000103 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.1 on 9 degrees of freedom
## Multiple R-squared:  0.9951, Adjusted R-squared:  0.9946 
## F-statistic:  1833 on 1 and 9 DF,  p-value: 0.00000000001034

## [1] "SAR"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.091  -3.118   2.336   4.832  11.327 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4986.0909     6.1570   809.8 < 0.0000000000000002 ***
## op_count       8.6164     0.2081    41.4       0.000000000014 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.92 on 9 degrees of freedom
## Multiple R-squared:  0.9948, Adjusted R-squared:  0.9942 
## F-statistic:  1714 on 1 and 9 DF,  p-value: 0.00000000001396

## [1] "KECCAK256" "geth"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -324.14  -29.66   37.15   87.38  138.74 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4393.136     78.756   55.78    0.000000000000962 ***
## op_count     406.542      2.662  152.69 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 139.6 on 9 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 2.332e+04 on 1 and 9 DF,  p-value: < 0.00000000000000022

## [1] "ADDRESS" "geth"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.6818  -2.1818   0.3182   4.0682   6.3182 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3493.682      3.136  1114.1 <0.0000000000000002 ***
## op_count      20.200      0.106   190.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.559 on 9 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9997 
## F-statistic: 3.631e+04 on 1 and 9 DF,  p-value: < 0.00000000000000022

## [1] "ORIGIN" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.909  -5.209  -1.791  10.095  21.318 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3515.9091     8.8986  395.11 < 0.0000000000000002 ***
## op_count       6.3109     0.3008   20.98        0.00000000596 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.78 on 9 degrees of freedom
## Multiple R-squared:   0.98,  Adjusted R-squared:  0.9777 
## F-statistic: 440.1 on 1 and 9 DF,  p-value: 0.000000005956

## [1] "CALLER" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.664 -22.493  -4.823  -0.823 109.641 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3476.2500    23.4478  148.25 < 0.0000000000000002 ***
## op_count      11.4536     0.7927   14.45          0.000000156 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.57 on 9 degrees of freedom
## Multiple R-squared:  0.9587, Adjusted R-squared:  0.9541 
## F-statistic: 208.8 on 1 and 9 DF,  p-value: 0.000000156

## [1] "CALLVALUE" "geth"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.659  -2.450   2.696   5.552  11.555 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3469.6591     5.8828  589.80 < 0.0000000000000002 ***
## op_count       4.9573     0.1989   24.93        0.00000000129 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.43 on 9 degrees of freedom
## Multiple R-squared:  0.9857, Adjusted R-squared:  0.9841 
## F-statistic: 621.3 on 1 and 9 DF,  p-value: 0.000000001292

## [1] "CALLDATALOAD" "geth"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -114.26  -46.52   16.75   34.47  122.71 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10951.795     43.665  250.81 < 0.0000000000000002 ***
## op_count       33.499      1.476   22.69        0.00000000297 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 77.41 on 9 degrees of freedom
## Multiple R-squared:  0.9828, Adjusted R-squared:  0.9809 
## F-statistic:   515 on 1 and 9 DF,  p-value: 0.000000002972

## [1] "CALLDATASIZE" "geth"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.3864  -6.6909   0.5773   5.3273  11.3182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3464.3864     4.8057  720.89 < 0.0000000000000002 ***
## op_count       5.0518     0.1625   31.09        0.00000000018 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.52 on 9 degrees of freedom
## Multiple R-squared:  0.9908, Adjusted R-squared:  0.9898 
## F-statistic: 966.9 on 1 and 9 DF,  p-value: 0.0000000001804

## [1] "CALLDATACOPY" "geth"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -107.07  -39.41  -13.21   49.34   89.09 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10077.57      38.15   264.1 < 0.0000000000000002 ***
## op_count       39.73       1.29    30.8       0.000000000196 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 67.64 on 9 degrees of freedom
## Multiple R-squared:  0.9906, Adjusted R-squared:  0.9896 
## F-statistic: 948.8 on 1 and 9 DF,  p-value: 0.0000000001963

## [1] "CODESIZE" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -15.682  -7.609   1.209   6.141  13.791 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3469.6818     5.2766  657.56 < 0.0000000000000002 ***
## op_count       5.3055     0.1784   29.74       0.000000000268 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.354 on 9 degrees of freedom
## Multiple R-squared:  0.9899, Adjusted R-squared:  0.9888 
## F-statistic: 884.6 on 1 and 9 DF,  p-value: 0.0000000002683

## [1] "CODECOPY" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -157.25  -12.84    1.00   56.41  108.31 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10051.568     49.873   201.5 < 0.0000000000000002 ***
## op_count       26.637      1.686    15.8         0.0000000718 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 88.42 on 9 degrees of freedom
## Multiple R-squared:  0.9652, Adjusted R-squared:  0.9613 
## F-statistic: 249.6 on 1 and 9 DF,  p-value: 0.00000007182

## [1] "GASPRICE" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.895  -9.152  -1.427   6.843  50.591 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3471.9318    13.0653   265.7 < 0.0000000000000002 ***
## op_count       5.6991     0.4417    12.9          0.000000414 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.16 on 9 degrees of freedom
## Multiple R-squared:  0.9487, Adjusted R-squared:  0.943 
## F-statistic: 166.5 on 1 and 9 DF,  p-value: 0.0000004141

## [1] "EXTCODESIZE" "geth"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -140.805  -38.109   -7.777   54.884  103.655 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11895.750     44.120  269.62 < 0.0000000000000002 ***
## op_count       54.403      1.492   36.47      0.0000000000434 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 78.22 on 9 degrees of freedom
## Multiple R-squared:  0.9933, Adjusted R-squared:  0.9925 
## F-statistic:  1330 on 1 and 9 DF,  p-value: 0.00000000004335

## [1] "EXTCODECOPY" "geth"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -198.95  -85.04   -4.62   59.05  352.55 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 12218.614     91.264  133.88 0.000000000000000368 ***
## op_count       75.334      3.085   24.42 0.000000001552415127 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 161.8 on 9 degrees of freedom
## Multiple R-squared:  0.9851, Adjusted R-squared:  0.9835 
## F-statistic: 596.2 on 1 and 9 DF,  p-value: 0.000000001552

## [1] "RETURNDATASIZE" "geth"          
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.236  -9.155  -0.518   4.009  48.655 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3478.9545    12.5158  277.96 < 0.0000000000000002 ***
## op_count       4.3891     0.4231   10.37           0.00000264 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.19 on 9 degrees of freedom
## Multiple R-squared:  0.9228, Adjusted R-squared:  0.9142 
## F-statistic: 107.6 on 1 and 9 DF,  p-value: 0.000002635

## [1] "RETURNDATACOPY" "geth"          
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -585.34  -26.03    8.58   90.47  344.29 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 13476.591    143.700  93.783 0.00000000000000904 ***
## op_count        6.158      4.858   1.268               0.237    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 254.8 on 9 degrees of freedom
## Multiple R-squared:  0.1515, Adjusted R-squared:  0.05722 
## F-statistic: 1.607 on 1 and 9 DF,  p-value: 0.2367

## [1] "EXTCODEHASH" "geth"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -114.92  -92.61  -27.86   85.59  156.03 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11935.000     59.548  200.43 < 0.0000000000000002 ***
## op_count       80.295      2.013   39.89      0.0000000000195 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 105.6 on 9 degrees of freedom
## Multiple R-squared:  0.9944, Adjusted R-squared:  0.9937 
## F-statistic:  1591 on 1 and 9 DF,  p-value: 0.00000000001947

## [1] "COINBASE" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.364  -7.936  -3.609   3.809  29.973 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3469.1818     7.4962  462.79 < 0.0000000000000002 ***
## op_count       7.5836     0.2534   29.93       0.000000000254 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.29 on 9 degrees of freedom
## Multiple R-squared:  0.9901, Adjusted R-squared:  0.9889 
## F-statistic: 895.5 on 1 and 9 DF,  p-value: 0.0000000002539

## [1] "TIMESTAMP" "geth"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.773 -16.214  -0.445   5.377  48.045 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3473.7727    14.6174 237.646 < 0.0000000000000002 ***
## op_count       4.6673     0.4942   9.445           0.00000574 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.91 on 9 degrees of freedom
## Multiple R-squared:  0.9084, Adjusted R-squared:  0.8982 
## F-statistic: 89.21 on 1 and 9 DF,  p-value: 0.000005744

## [1] "NUMBER" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.645 -16.605  -3.636   6.814  53.191 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3489.8182    14.4149   242.1 < 0.0000000000000002 ***
## op_count       5.2164     0.4873    10.7           0.00000202 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.55 on 9 degrees of freedom
## Multiple R-squared:  0.9272, Adjusted R-squared:  0.9191 
## F-statistic: 114.6 on 1 and 9 DF,  p-value: 0.000002025

## [1] "DIFFICULTY" "geth"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.664 -17.259  -0.355  15.800  48.991 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3462.0455    17.6627  196.01 < 0.0000000000000002 ***
## op_count       7.9655     0.5971   13.34          0.000000311 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31.31 on 9 degrees of freedom
## Multiple R-squared:  0.9519, Adjusted R-squared:  0.9465 
## F-statistic:   178 on 1 and 9 DF,  p-value: 0.000000311

## [1] "GASLIMIT" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43.06 -25.73 -10.89  18.11  77.83 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3453.5000    21.2646 162.406 < 0.0000000000000002 ***
## op_count       5.4891     0.7189   7.636            0.0000321 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.7 on 9 degrees of freedom
## Multiple R-squared:  0.8663, Adjusted R-squared:  0.8514 
## F-statistic:  58.3 on 1 and 9 DF,  p-value: 0.00003206

## [1] "CHAINID" "geth"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.068 -28.291  -8.305  25.486  64.564 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3450.0682    21.6164  159.60 < 0.0000000000000002 ***
## op_count       7.8736     0.7308   10.77           0.00000192 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 38.32 on 9 degrees of freedom
## Multiple R-squared:  0.9281, Adjusted R-squared:  0.9201 
## F-statistic: 116.1 on 1 and 9 DF,  p-value: 0.000001917

## [1] "SELFBALANCE" "geth"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -58.38 -21.63 -12.15  23.87  64.40 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3450.6591    24.0016  143.77 < 0.0000000000000002 ***
## op_count      33.9555     0.8114   41.85      0.0000000000127 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.55 on 9 degrees of freedom
## Multiple R-squared:  0.9949, Adjusted R-squared:  0.9943 
## F-statistic:  1751 on 1 and 9 DF,  p-value: 0.00000000001267

## [1] "POP"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -65.986 -32.736  -7.523  39.116  75.032 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4141.4318    27.4429 150.911 < 0.0000000000000002 ***
## op_count       6.3518     0.9277   6.847             0.000075 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 48.65 on 9 degrees of freedom
## Multiple R-squared:  0.8389, Adjusted R-squared:  0.821 
## F-statistic: 46.88 on 1 and 9 DF,  p-value: 0.00007502

## [1] "MLOAD" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -249.98  -41.05   11.22   87.62  164.82 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10909.977     76.432 142.741 < 0.0000000000000002 ***
## op_count       13.090      2.584   5.066             0.000675 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 135.5 on 9 degrees of freedom
## Multiple R-squared:  0.7404, Adjusted R-squared:  0.7115 
## F-statistic: 25.66 on 1 and 9 DF,  p-value: 0.0006755

## [1] "MSTORE" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.095 -23.168  -4.886   8.330  62.832 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3352.8864    19.0456  176.04 < 0.0000000000000002 ***
## op_count      15.5427     0.6439   24.14        0.00000000172 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.76 on 9 degrees of freedom
## Multiple R-squared:  0.9848, Adjusted R-squared:  0.9831 
## F-statistic: 582.7 on 1 and 9 DF,  p-value: 0.000000001718

## [1] "MSTORE_COLD" "geth"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42.84 -35.75 -10.66  22.83  83.17 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4192.8409    23.6485   177.3 < 0.0000000000000002 ***
## op_count      20.0664     0.7995    25.1        0.00000000122 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.92 on 9 degrees of freedom
## Multiple R-squared:  0.9859, Adjusted R-squared:  0.9844 
## F-statistic:   630 on 1 and 9 DF,  p-value: 0.000000001215

## [1] "MSTORE8" "geth"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -132.27  -33.65    4.00   34.32  109.30 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9903.159     37.373  264.98 < 0.0000000000000002 ***
## op_count      15.714      1.263   12.44          0.000000567 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66.26 on 9 degrees of freedom
## Multiple R-squared:  0.945,  Adjusted R-squared:  0.9389 
## F-statistic: 154.7 on 1 and 9 DF,  p-value: 0.0000005674

## [1] "JUMP" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -189.23  -23.22   21.16   53.09   77.96 
## 
## Coefficients:
##             Estimate Std. Error t value           Pr(>|t|)    
## (Intercept) 3735.727     46.168  80.916 0.0000000000000341 ***
## op_count      13.087      1.561   8.385 0.0000151738248240 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81.85 on 9 degrees of freedom
## Multiple R-squared:  0.8865, Adjusted R-squared:  0.8739 
## F-statistic: 70.31 on 1 and 9 DF,  p-value: 0.00001517

## [1] "JUMPI" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -378.93  -45.40   43.40   73.18  176.74 
## 
## Coefficients:
##             Estimate Std. Error t value          Pr(>|t|)    
## (Intercept) 5428.932     90.729   59.84 0.000000000000512 ***
## op_count      16.255      3.067    5.30          0.000494 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 160.8 on 9 degrees of freedom
## Multiple R-squared:  0.7573, Adjusted R-squared:  0.7304 
## F-statistic: 28.09 on 1 and 9 DF,  p-value: 0.000494

## [1] "PC"   "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27.64 -20.96 -11.34  17.62  48.69 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3466.5909    16.2917 212.782 < 0.0000000000000002 ***
## op_count       4.7055     0.5508   8.544             0.000013 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.88 on 9 degrees of freedom
## Multiple R-squared:  0.8902, Adjusted R-squared:  0.878 
## F-statistic: 72.99 on 1 and 9 DF,  p-value: 0.00001304

## [1] "MSIZE" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -32.86 -22.14 -14.43  19.24  53.10 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3487.341     16.950 205.741 < 0.0000000000000002 ***
## op_count       4.152      0.573   7.246            0.0000484 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.05 on 9 degrees of freedom
## Multiple R-squared:  0.8537, Adjusted R-squared:  0.8374 
## F-statistic:  52.5 on 1 and 9 DF,  p-value: 0.0000484

## [1] "GAS"  "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38.60 -26.52 -11.99  27.89  59.45 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3458.7273    20.7778 166.463 < 0.0000000000000002 ***
## op_count       5.1218     0.7024   7.292            0.0000461 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.84 on 9 degrees of freedom
## Multiple R-squared:  0.8552, Adjusted R-squared:  0.8391 
## F-statistic: 53.17 on 1 and 9 DF,  p-value: 0.00004605

## [1] "JUMPDEST" "geth"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.973 -12.786  -1.482  11.250  35.536 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 2635.0455    12.4764  211.20 < 0.0000000000000002 ***
## op_count       4.3982     0.4218   10.43           0.00000252 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.12 on 9 degrees of freedom
## Multiple R-squared:  0.9236, Adjusted R-squared:  0.9151 
## F-statistic: 108.7 on 1 and 9 DF,  p-value: 0.000002522

## [1] "MCOPY" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -98.386 -27.307  -2.532  31.984  98.823 
## 
## Coefficients:
##             Estimate Std. Error t value           Pr(>|t|)    
## (Intercept) 3802.386     33.204   114.5 0.0000000000000015 ***
## op_count      21.665      1.122    19.3 0.0000000124271686 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 58.86 on 9 degrees of freedom
## Multiple R-squared:  0.9764, Adjusted R-squared:  0.9738 
## F-statistic: 372.5 on 1 and 9 DF,  p-value: 0.00000001243

## [1] "MCOPY_COLD" "geth"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -92.545 -39.568  -4.464  46.405  78.218 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4455.045     32.482  137.15 0.000000000000000296 ***
## op_count      26.647      1.098   24.27 0.000000001639893813 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57.58 on 9 degrees of freedom
## Multiple R-squared:  0.9849, Adjusted R-squared:  0.9833 
## F-statistic: 588.9 on 1 and 9 DF,  p-value: 0.00000000164

## [1] "PUSH0" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43.98 -18.73 -14.23  22.52  50.27 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3462.9773    19.4738 177.827 < 0.0000000000000002 ***
## op_count       3.8500     0.6583   5.848             0.000244 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.52 on 9 degrees of freedom
## Multiple R-squared:  0.7917, Adjusted R-squared:  0.7685 
## F-statistic:  34.2 on 1 and 9 DF,  p-value: 0.0002443

## [1] "LOG0" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -752.7 -169.4  -47.2  212.7  753.0 
## 
## Coefficients:
##             Estimate Std. Error t value       Pr(>|t|)    
## (Intercept) 3798.795    245.483   15.47 0.000000086037 ***
## op_count     213.441      8.299   25.72 0.000000000978 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 435.2 on 9 degrees of freedom
## Multiple R-squared:  0.9866, Adjusted R-squared:  0.9851 
## F-statistic: 661.5 on 1 and 9 DF,  p-value: 0.0000000009784

## [1] "LOG1" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -460.64  -79.75  -12.18   87.09  519.45 
## 
## Coefficients:
##             Estimate Std. Error t value        Pr(>|t|)    
## (Intercept) 4245.455    162.591   26.11 0.0000000008553 ***
## op_count     235.255      5.497   42.80 0.0000000000104 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 288.2 on 9 degrees of freedom
## Multiple R-squared:  0.9951, Adjusted R-squared:  0.9946 
## F-statistic:  1832 on 1 and 9 DF,  p-value: 0.00000000001036

## [1] "LOG2" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -386.5 -146.8  -27.5  102.9  503.7 
## 
## Coefficients:
##             Estimate Std. Error t value         Pr(>|t|)    
## (Intercept)  4636.09     158.26   29.30 0.00000000030705 ***
## op_count      254.38       5.35   47.55 0.00000000000403 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 280.6 on 9 degrees of freedom
## Multiple R-squared:  0.996,  Adjusted R-squared:  0.9956 
## F-statistic:  2261 on 1 and 9 DF,  p-value: 0.000000000004034

## [1] "LOG3" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -278.73 -138.50   -8.68  128.59  363.00 
## 
## Coefficients:
##             Estimate Std. Error t value          Pr(>|t|)    
## (Intercept) 5045.955    116.297   43.39 0.000000000009163 ***
## op_count     267.573      3.932   68.06 0.000000000000161 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 206.2 on 9 degrees of freedom
## Multiple R-squared:  0.9981, Adjusted R-squared:  0.9978 
## F-statistic:  4632 on 1 and 9 DF,  p-value: 0.0000000000001613

## [1] "LOG4" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -365.91 -161.98   -5.86  122.73  350.27 
## 
## Coefficients:
##             Estimate Std. Error t value          Pr(>|t|)    
## (Intercept) 5528.182    138.058   40.04 0.000000000018804 ***
## op_count     278.936      4.667   59.77 0.000000000000518 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 244.8 on 9 degrees of freedom
## Multiple R-squared:  0.9975, Adjusted R-squared:  0.9972 
## F-statistic:  3572 on 1 and 9 DF,  p-value: 0.0000000000005181

## [1] "CREATE" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2725.6  -763.8   120.1   958.1  2321.2 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)  5238.82     858.46   6.103             0.000179 ***
## op_count     7374.92      29.02 254.121 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1522 on 9 degrees of freedom
## Multiple R-squared:  0.9999, Adjusted R-squared:  0.9998 
## F-statistic: 6.458e+04 on 1 and 9 DF,  p-value: < 0.00000000000000022

## [1] "CALL" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -374.9 -258.7  -16.9  137.6  728.1 
## 
## Coefficients:
##              Estimate Std. Error t value           Pr(>|t|)    
## (Intercept) 16929.386    207.450   81.61 0.0000000000000315 ***
## op_count      492.701      7.013   70.25 0.0000000000001212 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 367.8 on 9 degrees of freedom
## Multiple R-squared:  0.9982, Adjusted R-squared:  0.998 
## F-statistic:  4936 on 1 and 9 DF,  p-value: 0.0000000000001212

## [1] "RETURN" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -800.2 -317.3  152.5  288.2  593.4 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 42595.341    261.383 162.962 <0.0000000000000002 ***
## op_count       24.008      8.836   2.717              0.0237 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 463.4 on 9 degrees of freedom
## Multiple R-squared:  0.4506, Adjusted R-squared:  0.3896 
## F-statistic: 7.382 on 1 and 9 DF,  p-value: 0.02372

## [1] "DELEGATECALL" "geth"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -524.73 -117.38   -2.01  177.40  449.88 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 15644.727    158.889   98.46 0.00000000000000583 ***
## op_count      384.895      5.371   71.66 0.00000000000010150 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 281.7 on 9 degrees of freedom
## Multiple R-squared:  0.9983, Adjusted R-squared:  0.9981 
## F-statistic:  5135 on 1 and 9 DF,  p-value: 0.0000000000001015

## [1] "STATICCALL" "geth"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -527.52 -239.95   43.71  217.50  552.58 
## 
## Coefficients:
##              Estimate Std. Error t value           Pr(>|t|)    
## (Intercept) 15827.023    198.251   79.83 0.0000000000000384 ***
## op_count      436.326      6.702   65.10 0.0000000000002403 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 351.5 on 9 degrees of freedom
## Multiple R-squared:  0.9979, Adjusted R-squared:  0.9976 
## F-statistic:  4238 on 1 and 9 DF,  p-value: 0.0000000000002403

## [1] "REVERT" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -318.09 -106.19   38.11  112.01  305.31 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 42797.591    108.001  396.27 < 0.0000000000000002 ***
## op_count       63.060      3.651   17.27          0.000000033 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 191.5 on 9 degrees of freedom
## Multiple R-squared:  0.9707, Adjusted R-squared:  0.9675 
## F-statistic: 298.3 on 1 and 9 DF,  p-value: 0.00000003297

## [1] "PUSH1" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.8864  -3.8114  -0.2955   4.2477  11.6727 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3420.3864     4.5923  744.81 < 0.0000000000000002 ***
## op_count       5.9882     0.1552   38.57      0.0000000000263 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.141 on 9 degrees of freedom
## Multiple R-squared:  0.994,  Adjusted R-squared:  0.9933 
## F-statistic:  1488 on 1 and 9 DF,  p-value: 0.00000000002629

## [1] "PUSH2" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.586 -15.220   0.623  13.582  39.832 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3413.7500    13.3610  255.50 < 0.0000000000000002 ***
## op_count       9.9209     0.4517   21.96        0.00000000397 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.69 on 9 degrees of freedom
## Multiple R-squared:  0.9817, Adjusted R-squared:  0.9797 
## F-statistic: 482.4 on 1 and 9 DF,  p-value: 0.000000003968

## [1] "PUSH3" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -65.773 -16.836   3.455  26.555  39.300 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3394.7727    18.5501  183.00 < 0.0000000000000002 ***
## op_count       9.9855     0.6271   15.92         0.0000000671 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.89 on 9 degrees of freedom
## Multiple R-squared:  0.9657, Adjusted R-squared:  0.9619 
## F-statistic: 253.5 on 1 and 9 DF,  p-value: 0.00000006709

## [1] "PUSH4" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.886 -13.832   1.668  16.557  35.132 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3402.3864    15.0227  226.48 < 0.0000000000000002 ***
## op_count       9.9482     0.5079   19.59         0.0000000109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.63 on 9 degrees of freedom
## Multiple R-squared:  0.9771, Adjusted R-squared:  0.9745 
## F-statistic: 383.7 on 1 and 9 DF,  p-value: 0.00000001091

## [1] "PUSH5" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.386  -3.293   2.959   9.227  11.727 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3414.8864     6.9494   491.4 < 0.0000000000000002 ***
## op_count       9.1155     0.2349    38.8      0.0000000000249 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.32 on 9 degrees of freedom
## Multiple R-squared:  0.9941, Adjusted R-squared:  0.9934 
## F-statistic:  1505 on 1 and 9 DF,  p-value: 0.00000000002493

## [1] "PUSH6" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.7955  -7.8477   0.8455   7.7136  16.3364 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3406.796      6.507  523.53 < 0.0000000000000002 ***
## op_count      10.024      0.220   45.57      0.0000000000059 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.54 on 9 degrees of freedom
## Multiple R-squared:  0.9957, Adjusted R-squared:  0.9952 
## F-statistic:  2077 on 1 and 9 DF,  p-value: 0.000000000005904

## [1] "PUSH7" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.0682  -3.5432   0.7636   5.8182  13.2091 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3416.5682     5.5497  615.63 < 0.0000000000000002 ***
## op_count      10.1445     0.1876   54.07     0.00000000000127 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.839 on 9 degrees of freedom
## Multiple R-squared:  0.9969, Adjusted R-squared:  0.9966 
## F-statistic:  2924 on 1 and 9 DF,  p-value: 0.000000000001273

## [1] "PUSH8" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.827 -13.132  -9.523   5.868  81.709 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3409.7045    20.5182  166.18 < 0.0000000000000002 ***
## op_count      10.6464     0.6936   15.35         0.0000000924 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.37 on 9 degrees of freedom
## Multiple R-squared:  0.9632, Adjusted R-squared:  0.9591 
## F-statistic: 235.6 on 1 and 9 DF,  p-value: 0.00000009239

## [1] "PUSH9" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38.95 -18.00  -6.00  12.78  60.51 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3426.5455    16.8432  203.44 < 0.0000000000000002 ***
## op_count       9.0982     0.5694   15.98         0.0000000651 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29.86 on 9 degrees of freedom
## Multiple R-squared:  0.9659, Adjusted R-squared:  0.9622 
## F-statistic: 255.3 on 1 and 9 DF,  p-value: 0.00000006509

## [1] "PUSH10" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -71.182 -49.682   1.136  48.682  71.500 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3342.89      31.66  105.60 0.00000000000000311 ***
## op_count       14.73       1.07   13.77 0.00000023730736255 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56.12 on 9 degrees of freedom
## Multiple R-squared:  0.9547, Adjusted R-squared:  0.9496 
## F-statistic: 189.5 on 1 and 9 DF,  p-value: 0.0000002373

## [1] "PUSH11" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.864  -4.295  -2.673   3.986  18.200 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3423.3636     4.8425  706.95 < 0.0000000000000002 ***
## op_count       9.4873     0.1637   57.95    0.000000000000683 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.585 on 9 degrees of freedom
## Multiple R-squared:  0.9973, Adjusted R-squared:  0.997 
## F-statistic:  3359 on 1 and 9 DF,  p-value: 0.000000000000683

## [1] "PUSH12" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.1136  -7.8636  -0.8636   6.6864  16.4364 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3423.6136     6.3931  535.52 < 0.0000000000000002 ***
## op_count       8.7700     0.2161   40.58      0.0000000000167 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.33 on 9 degrees of freedom
## Multiple R-squared:  0.9946, Adjusted R-squared:  0.994 
## F-statistic:  1647 on 1 and 9 DF,  p-value: 0.00000000001669

## [1] "PUSH13" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.636  -6.509   5.191   8.673  11.500 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3406.136      7.128  477.84 < 0.0000000000000002 ***
## op_count      10.134      0.241   42.06      0.0000000000121 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.64 on 9 degrees of freedom
## Multiple R-squared:  0.9949, Adjusted R-squared:  0.9944 
## F-statistic:  1769 on 1 and 9 DF,  p-value: 0.00000000001212

## [1] "PUSH14" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.477  -5.584   4.741   8.545  14.132 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3417.4773     7.9395  430.44 < 0.0000000000000002 ***
## op_count       9.9391     0.2684   37.03      0.0000000000379 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.08 on 9 degrees of freedom
## Multiple R-squared:  0.9935, Adjusted R-squared:  0.9928 
## F-statistic:  1371 on 1 and 9 DF,  p-value: 0.00000000003787

## [1] "PUSH15" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.864  -2.954   2.773   8.836  23.400 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3414.8636     9.6391  354.27 < 0.0000000000000002 ***
## op_count      10.4473     0.3259   32.06       0.000000000137 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.09 on 9 degrees of freedom
## Multiple R-squared:  0.9913, Adjusted R-squared:  0.9904 
## F-statistic:  1028 on 1 and 9 DF,  p-value: 0.0000000001373

## [1] "PUSH16" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.773 -13.318  -0.818  21.341  32.273 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3415.2727    14.8597  229.84 < 0.0000000000000002 ***
## op_count       8.8182     0.5023   17.55         0.0000000286 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.34 on 9 degrees of freedom
## Multiple R-squared:  0.9716, Adjusted R-squared:  0.9685 
## F-statistic: 308.1 on 1 and 9 DF,  p-value: 0.0000000286

## [1] "PUSH17" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.200 -29.832   5.727  14.659  62.809 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3401.5455    19.6307  173.28 < 0.0000000000000002 ***
## op_count      10.0145     0.6636   15.09          0.000000107 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.8 on 9 degrees of freedom
## Multiple R-squared:  0.962,  Adjusted R-squared:  0.9578 
## F-statistic: 227.7 on 1 and 9 DF,  p-value: 0.0000001071

## [1] "PUSH18" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.600 -18.541  -5.982   9.405  57.345 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3400.5455    15.8826  214.10 < 0.0000000000000002 ***
## op_count      10.1109     0.5369   18.83         0.0000000154 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.16 on 9 degrees of freedom
## Multiple R-squared:  0.9752, Adjusted R-squared:  0.9725 
## F-statistic: 354.6 on 1 and 9 DF,  p-value: 0.00000001543

## [1] "PUSH19" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.864  -5.759   2.200   7.423  12.454 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3401.8636     6.2877  541.03 < 0.0000000000000002 ***
## op_count      10.7873     0.2126   50.75     0.00000000000225 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.15 on 9 degrees of freedom
## Multiple R-squared:  0.9965, Adjusted R-squared:  0.9961 
## F-statistic:  2575 on 1 and 9 DF,  p-value: 0.000000000002248

## [1] "PUSH20" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.0227  -3.5682   0.4091   8.5455  10.5909 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3402.0227     5.8087  585.67 < 0.0000000000000002 ***
## op_count       9.9682     0.1964   50.76     0.00000000000224 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.3 on 9 degrees of freedom
## Multiple R-squared:  0.9965, Adjusted R-squared:  0.9961 
## F-statistic:  2577 on 1 and 9 DF,  p-value: 0.000000000002243

## [1] "PUSH21" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.682  -1.750   2.273   4.273  32.282 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3401.7727     9.3412  364.17 < 0.0000000000000002 ***
## op_count      10.1982     0.3158   32.29       0.000000000129 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.56 on 9 degrees of freedom
## Multiple R-squared:  0.9914, Adjusted R-squared:  0.9905 
## F-statistic:  1043 on 1 and 9 DF,  p-value: 0.0000000001287

## [1] "PUSH22" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.114  -3.966   0.955  12.239  16.227 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3412.6136     8.9141  382.83 < 0.0000000000000002 ***
## op_count      11.5318     0.3014   38.27      0.0000000000282 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.8 on 9 degrees of freedom
## Multiple R-squared:  0.9939, Adjusted R-squared:  0.9932 
## F-statistic:  1464 on 1 and 9 DF,  p-value: 0.00000000002822

## [1] "PUSH23" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.614 -11.336   2.905   9.659  31.391 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3402.1136    10.3301   329.3 < 0.0000000000000002 ***
## op_count      11.6991     0.3492    33.5      0.0000000000927 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.31 on 9 degrees of freedom
## Multiple R-squared:  0.992,  Adjusted R-squared:  0.9912 
## F-statistic:  1122 on 1 and 9 DF,  p-value: 0.00000000009273

## [1] "PUSH24" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.673 -16.318  -7.964  10.182  54.918 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3396.9091    14.4138   235.7 < 0.0000000000000002 ***
## op_count      10.5764     0.4873    21.7        0.00000000441 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.55 on 9 degrees of freedom
## Multiple R-squared:  0.9813, Adjusted R-squared:  0.9792 
## F-statistic: 471.1 on 1 and 9 DF,  p-value: 0.000000004408

## [1] "PUSH25" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.341  -8.829   2.136   9.693  16.454 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3405.3409     8.1720  416.71 < 0.0000000000000002 ***
## op_count       9.9682     0.2763   36.08      0.0000000000478 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.49 on 9 degrees of freedom
## Multiple R-squared:  0.9931, Adjusted R-squared:  0.9924 
## F-statistic:  1302 on 1 and 9 DF,  p-value: 0.00000000004775

## [1] "PUSH26" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.227  -6.627   2.273   7.623  13.473 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3405.7273     5.8296  584.21 < 0.0000000000000002 ***
## op_count      10.3600     0.1971   52.57     0.00000000000164 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.33 on 9 degrees of freedom
## Multiple R-squared:  0.9968, Adjusted R-squared:  0.9964 
## F-statistic:  2763 on 1 and 9 DF,  p-value: 0.000000000001639

## [1] "PUSH27" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.296  -3.280   3.186   6.566  12.600 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3413.2955     6.5522  520.93 < 0.0000000000000002 ***
## op_count       9.9173     0.2215   44.77     0.00000000000692 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.62 on 9 degrees of freedom
## Multiple R-squared:  0.9955, Adjusted R-squared:  0.995 
## F-statistic:  2005 on 1 and 9 DF,  p-value: 0.000000000006916

## [1] "PUSH28" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.636  -6.295   2.546  12.750  16.591 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3412.3636     9.5525  357.22 < 0.0000000000000002 ***
## op_count      10.2727     0.3229   31.81       0.000000000147 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.93 on 9 degrees of freedom
## Multiple R-squared:  0.9912, Adjusted R-squared:  0.9902 
## F-statistic:  1012 on 1 and 9 DF,  p-value: 0.0000000001472

## [1] "PUSH29" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.882  -9.791  -2.727   3.636  26.691 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3417.7727     8.3980  406.98 < 0.0000000000000002 ***
## op_count      10.3691     0.2839   36.52      0.0000000000428 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.89 on 9 degrees of freedom
## Multiple R-squared:  0.9933, Adjusted R-squared:  0.9926 
## F-statistic:  1334 on 1 and 9 DF,  p-value: 0.00000000004283

## [1] "PUSH30" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.409 -17.277   3.055  11.982  67.518 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3409.091     18.458  184.70 < 0.0000000000000002 ***
## op_count      10.496      0.624   16.82         0.0000000415 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.72 on 9 degrees of freedom
## Multiple R-squared:  0.9692, Adjusted R-squared:  0.9657 
## F-statistic:   283 on 1 and 9 DF,  p-value: 0.00000004154

## [1] "PUSH31" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.000  -5.409  -1.446   6.859  14.609 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3411.5000     6.1343  556.14 < 0.0000000000000002 ***
## op_count      11.1964     0.2074   53.99     0.00000000000129 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.87 on 9 degrees of freedom
## Multiple R-squared:  0.9969, Adjusted R-squared:  0.9966 
## F-statistic:  2915 on 1 and 9 DF,  p-value: 0.00000000000129

## [1] "PUSH32" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.250  -5.705   2.546   8.648  11.264 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 3405.2500     5.4755  621.90 < 0.0000000000000002 ***
## op_count      10.7282     0.1851   57.96    0.000000000000683 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.707 on 9 degrees of freedom
## Multiple R-squared:  0.9973, Adjusted R-squared:  0.997 
## F-statistic:  3359 on 1 and 9 DF,  p-value: 0.0000000000006826

## [1] "DUP1" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.159 -5.973 -2.732  5.425 12.546 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4888.6591     4.5159 1082.54 < 0.0000000000000002 ***
## op_count       5.7518     0.1527   37.68      0.0000000000324 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.006 on 9 degrees of freedom
## Multiple R-squared:  0.9937, Adjusted R-squared:  0.993 
## F-statistic:  1419 on 1 and 9 DF,  p-value: 0.00000000003244

## [1] "DUP2" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.023 -3.375  1.182  2.511  7.068 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4887.02273    2.91501 1676.50 < 0.0000000000000002 ***
## op_count       5.59545    0.09855   56.78    0.000000000000821 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.168 on 9 degrees of freedom
## Multiple R-squared:  0.9972, Adjusted R-squared:  0.9969 
## F-statistic:  3224 on 1 and 9 DF,  p-value: 0.0000000000008206

## [1] "DUP3" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -28.14 -14.03 -10.10  11.76  55.44 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4905.9545    15.1576  323.66 < 0.0000000000000002 ***
## op_count       5.3036     0.5124   10.35           0.00000268 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.87 on 9 degrees of freedom
## Multiple R-squared:  0.9225, Adjusted R-squared:  0.9139 
## F-statistic: 107.1 on 1 and 9 DF,  p-value: 0.000002685

## [1] "DUP4" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.427 -22.836  -3.109  22.268  44.109 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4890.182     16.860 290.049 < 0.0000000000000002 ***
## op_count       5.193      0.570   9.111           0.00000773 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29.89 on 9 degrees of freedom
## Multiple R-squared:  0.9022, Adjusted R-squared:  0.8913 
## F-statistic:    83 on 1 and 9 DF,  p-value: 0.000007726

## [1] "DUP5" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -47.064 -20.823   6.955  16.786  51.418 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4920.1364    18.3295 268.427 < 0.0000000000000002 ***
## op_count       4.5964     0.6197   7.418            0.0000403 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.49 on 9 degrees of freedom
## Multiple R-squared:  0.8594, Adjusted R-squared:  0.8438 
## F-statistic: 55.02 on 1 and 9 DF,  p-value: 0.00004027

## [1] "DUP6" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.159  -3.066   1.014   6.434  11.677 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4878.6591     6.1188  797.32 < 0.0000000000000002 ***
## op_count       5.7664     0.2069   27.88       0.000000000478 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.85 on 9 degrees of freedom
## Multiple R-squared:  0.9886, Adjusted R-squared:  0.9873 
## F-statistic: 777.1 on 1 and 9 DF,  p-value: 0.0000000004778

## [1] "DUP7" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.546  -7.727   1.609   4.773  20.618 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4892.0455     6.4073  763.51 < 0.0000000000000002 ***
## op_count       5.8673     0.2166   27.09       0.000000000617 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.36 on 9 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9865 
## F-statistic: 733.7 on 1 and 9 DF,  p-value: 0.0000000006171

## [1] "DUP8" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.954  -3.841   2.082   3.954  14.827 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4906.9545     5.1664   949.8 < 0.0000000000000002 ***
## op_count       6.0436     0.1747    34.6      0.0000000000694 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.159 on 9 degrees of freedom
## Multiple R-squared:  0.9925, Adjusted R-squared:  0.9917 
## F-statistic:  1197 on 1 and 9 DF,  p-value: 0.00000000006943

## [1] "DUP9" "geth"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.091 -13.591   1.636  12.591  19.454 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4870.2727     9.5169  511.75 < 0.0000000000000002 ***
## op_count       6.1545     0.3217   19.13         0.0000000134 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.87 on 9 degrees of freedom
## Multiple R-squared:  0.976,  Adjusted R-squared:  0.9733 
## F-statistic: 365.9 on 1 and 9 DF,  p-value: 0.00000001344

## [1] "DUP10" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.500 -11.027   1.409   6.955  29.582 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4874.5000     8.9691   543.5 < 0.0000000000000002 ***
## op_count       5.9418     0.3032    19.6         0.0000000109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.9 on 9 degrees of freedom
## Multiple R-squared:  0.9771, Adjusted R-squared:  0.9746 
## F-statistic:   384 on 1 and 9 DF,  p-value: 0.00000001087

## [1] "DUP11" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -61.09 -24.10  11.77  22.55  53.05 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4917.3864    20.3452 241.698 < 0.0000000000000002 ***
## op_count       6.4282     0.6878   9.346           0.00000626 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.07 on 9 degrees of freedom
## Multiple R-squared:  0.9066, Adjusted R-squared:  0.8962 
## F-statistic: 87.35 on 1 and 9 DF,  p-value: 0.000006264

## [1] "DUP12" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -52.48 -15.78  16.66  17.91  35.42 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4864.7045    17.4851  278.22 < 0.0000000000000002 ***
## op_count       5.9755     0.5911   10.11           0.00000327 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31 on 9 degrees of freedom
## Multiple R-squared:  0.9191, Adjusted R-squared:  0.9101 
## F-statistic: 102.2 on 1 and 9 DF,  p-value: 0.000003269

## [1] "DUP13" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.0682 -2.3864 -0.8864  1.1932  9.5455 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4899.0682     3.0352 1614.09 < 0.0000000000000002 ***
## op_count       5.4773     0.1026   53.38     0.00000000000143 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.381 on 9 degrees of freedom
## Multiple R-squared:  0.9969, Adjusted R-squared:  0.9965 
## F-statistic:  2849 on 1 and 9 DF,  p-value: 0.000000000001428

## [1] "DUP14" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.500  -3.482   3.773   4.718  12.218 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4902.000      5.531  886.26 < 0.0000000000000002 ***
## op_count       5.689      0.187   30.43       0.000000000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.806 on 9 degrees of freedom
## Multiple R-squared:  0.9904, Adjusted R-squared:  0.9893 
## F-statistic: 925.7 on 1 and 9 DF,  p-value: 0.0000000002191

## [1] "DUP15" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.454  -3.755   2.546   7.146   9.445 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4877.9545     5.2416  930.63 < 0.0000000000000002 ***
## op_count       5.7200     0.1772   32.28       0.000000000129 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.292 on 9 degrees of freedom
## Multiple R-squared:  0.9914, Adjusted R-squared:  0.9905 
## F-statistic:  1042 on 1 and 9 DF,  p-value: 0.0000000001292

## [1] "DUP16" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -60.250 -13.364  -7.795   6.966 125.545 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4968.7955    28.0914 176.879 < 0.0000000000000002 ***
## op_count       6.5591     0.9497   6.907            0.0000701 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49.8 on 9 degrees of freedom
## Multiple R-squared:  0.8413, Adjusted R-squared:  0.8236 
## F-statistic:  47.7 on 1 and 9 DF,  p-value: 0.00007014

## [1] "SWAP1" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -46.859 -18.784   7.905  18.339  26.518 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4062.1136    14.9448  271.81 < 0.0000000000000002 ***
## op_count       6.4245     0.5052   12.72          0.000000469 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.49 on 9 degrees of freedom
## Multiple R-squared:  0.9473, Adjusted R-squared:  0.9414 
## F-statistic: 161.7 on 1 and 9 DF,  p-value: 0.0000004692

## [1] "SWAP2" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.704  -3.266  -1.395   4.700   9.209 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4079.7045     3.8106 1070.62 < 0.0000000000000002 ***
## op_count       6.2173     0.1288   48.26     0.00000000000353 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.755 on 9 degrees of freedom
## Multiple R-squared:  0.9962, Adjusted R-squared:  0.9957 
## F-statistic:  2329 on 1 and 9 DF,  p-value: 0.000000000003528

## [1] "SWAP3" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.682 -3.818 -2.546  3.273 15.182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4079.0455     4.1621  980.04 < 0.0000000000000002 ***
## op_count       6.1818     0.1407   43.94     0.00000000000819 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.379 on 9 degrees of freedom
## Multiple R-squared:  0.9954, Adjusted R-squared:  0.9948 
## F-statistic:  1930 on 1 and 9 DF,  p-value: 0.000000000008191

## [1] "SWAP4" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.155 -12.682   1.791  10.145  66.136 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4058.0000    17.1025  237.28 < 0.0000000000000002 ***
## op_count       7.3473     0.5782   12.71          0.000000472 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.32 on 9 degrees of freedom
## Multiple R-squared:  0.9472, Adjusted R-squared:  0.9413 
## F-statistic: 161.5 on 1 and 9 DF,  p-value: 0.0000004719

## [1] "SWAP5" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.2045  -4.0455  -0.7136   2.7864  16.2909 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4089.2045     4.6310  883.01 < 0.0000000000000002 ***
## op_count       5.8336     0.1566   37.26      0.0000000000358 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.21 on 9 degrees of freedom
## Multiple R-squared:  0.9936, Adjusted R-squared:  0.9928 
## F-statistic:  1388 on 1 and 9 DF,  p-value: 0.00000000003581

## [1] "SWAP6" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.677 -13.520   0.532   9.136  40.741 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4100.8409    11.0261  371.92 < 0.0000000000000002 ***
## op_count       5.8209     0.3728   15.62         0.0000000795 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.55 on 9 degrees of freedom
## Multiple R-squared:  0.9644, Adjusted R-squared:  0.9605 
## F-statistic: 243.9 on 1 and 9 DF,  p-value: 0.00000007949

## [1] "SWAP7" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -86.273 -20.218   4.409  20.814  78.600 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4145.2727    27.3779 151.410 < 0.0000000000000002 ***
## op_count       6.1127     0.9255   6.604            0.0000988 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 48.54 on 9 degrees of freedom
## Multiple R-squared:  0.829,  Adjusted R-squared:   0.81 
## F-statistic: 43.62 on 1 and 9 DF,  p-value: 0.00009876

## [1] "SWAP8" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -46.73 -24.80  11.96  22.89  32.82 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4137.3636    17.1639  241.05 < 0.0000000000000002 ***
## op_count       5.8818     0.5802   10.14            0.0000032 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.43 on 9 degrees of freedom
## Multiple R-squared:  0.9195, Adjusted R-squared:  0.9105 
## F-statistic: 102.8 on 1 and 9 DF,  p-value: 0.000003195

## [1] "SWAP9" "geth" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -95.25 -67.95 -48.49  33.51 256.48 
## 
## Coefficients:
##             Estimate Std. Error t value          Pr(>|t|)    
## (Intercept) 4011.341     61.409  65.322 0.000000000000233 ***
## op_count      13.954      2.076   6.721 0.000086409600375 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108.9 on 9 degrees of freedom
## Multiple R-squared:  0.8339, Adjusted R-squared:  0.8154 
## F-statistic: 45.18 on 1 and 9 DF,  p-value: 0.00008641

## [1] "SWAP10" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -61.155 -16.666   0.927  21.805  57.055 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4234.3409    22.1793 190.914 < 0.0000000000000002 ***
## op_count       4.1209     0.7498   5.496             0.000382 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39.32 on 9 degrees of freedom
## Multiple R-squared:  0.7704, Adjusted R-squared:  0.7449 
## F-statistic: 30.21 on 1 and 9 DF,  p-value: 0.0003821

## [1] "SWAP11" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53.39 -31.48 -19.57  15.46 138.77 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4128.386     31.850 129.621 0.000000000000000492 ***
## op_count       5.114      1.077   4.749              0.00105 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56.46 on 9 degrees of freedom
## Multiple R-squared:  0.7148, Adjusted R-squared:  0.6831 
## F-statistic: 22.56 on 1 and 9 DF,  p-value: 0.001045

## [1] "SWAP12" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.582  -6.955  -3.209   7.791  27.546 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4093.7727     9.1570  447.06 < 0.0000000000000002 ***
## op_count       6.4873     0.3096   20.96        0.00000000601 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.23 on 9 degrees of freedom
## Multiple R-squared:  0.9799, Adjusted R-squared:  0.9777 
## F-statistic: 439.2 on 1 and 9 DF,  p-value: 0.000000006012

## [1] "SWAP13" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -70.036 -16.252  -7.282  19.086  88.591 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4068.9773    24.4206 166.621 < 0.0000000000000002 ***
## op_count       7.2373     0.8256   8.766            0.0000106 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.29 on 9 degrees of freedom
## Multiple R-squared:  0.8952, Adjusted R-squared:  0.8835 
## F-statistic: 76.85 on 1 and 9 DF,  p-value: 0.00001058

## [1] "SWAP14" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.136 -16.193  -4.568  14.807  55.227 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4094.477     15.500  264.15 < 0.0000000000000002 ***
## op_count       5.832      0.524   11.13           0.00000146 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27.48 on 9 degrees of freedom
## Multiple R-squared:  0.9323, Adjusted R-squared:  0.9247 
## F-statistic: 123.9 on 1 and 9 DF,  p-value: 0.000001459

## [1] "SWAP15" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8909 -4.2273 -0.8636  2.8591 11.8182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4170.1818     3.5384 1178.55 < 0.0000000000000002 ***
## op_count       6.0236     0.1196   50.36     0.00000000000241 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.273 on 9 degrees of freedom
## Multiple R-squared:  0.9965, Adjusted R-squared:  0.9961 
## F-statistic:  2536 on 1 and 9 DF,  p-value: 0.00000000000241

## [1] "SWAP16" "geth"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.3864  -1.5795  -0.4773   1.1705  10.3182 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4124.2045     3.6169 1140.26 < 0.0000000000000002 ***
## op_count       6.0591     0.1223   49.55     0.00000000000278 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.412 on 9 degrees of freedom
## Multiple R-squared:  0.9963, Adjusted R-squared:  0.9959 
## F-statistic:  2456 on 1 and 9 DF,  p-value: 0.000000000002784

The resulting estimates based on the regressions above.

estimates
##                 op estimate_marginal_ns estimate_marginal_ns_stderr  env
## 1              ADD           -51.440909                  4.00606261 geth
## 2              MUL            13.004545                  0.99019690 geth
## 3              SUB             5.244545                  2.00360875 geth
## 4              DIV             7.806364                  0.18376508 geth
## 5             SDIV             9.580000                  0.15556290 geth
## 6              MOD             7.410909                  0.24734699 geth
## 7             SMOD             9.315455                  0.14150282 geth
## 8           ADDMOD            12.614545                  0.26053276 geth
## 9           MULMOD            22.426364                  0.27870536 geth
## 10             EXP            26.974545                  0.38723384 geth
## 11      SIGNEXTEND             9.003636                  0.19296726 geth
## 12              LT             7.322727                  0.18791666 geth
## 13              GT             7.390000                  0.20104845 geth
## 14             SLT            10.649091                  1.17873584 geth
## 15             SGT             8.035455                  0.25727237 geth
## 16              EQ             7.010909                  0.24735441 geth
## 17          ISZERO             4.509091                  0.39113454 geth
## 18             AND             7.020909                  0.19982798 geth
## 19              OR             7.009091                  0.18487829 geth
## 20             XOR             6.971818                  0.22909953 geth
## 21             NOT             5.000000                  0.09550023 geth
## 22            BYTE             8.254545                  0.24385939 geth
## 23             SHL             7.913636                  0.38163476 geth
## 24             SHR             8.247273                  0.19265102 geth
## 25             SAR             8.616364                  0.20814366 geth
## 26       KECCAK256           406.541818                  2.66244611 geth
## 27         ADDRESS            20.200000                  0.10600865 geth
## 28          ORIGIN             6.310909                  0.30082775 geth
## 29          CALLER            11.453636                  0.79268111 geth
## 30       CALLVALUE             4.957273                  0.19887447 geth
## 31    CALLDATALOAD            33.499091                  1.47615635 geth
## 32    CALLDATASIZE             5.051818                  0.16246240 geth
## 33    CALLDATACOPY            39.728182                  1.28979675 geth
## 34        CODESIZE             5.305455                  0.17838269 geth
## 35        CODECOPY            26.637273                  1.68601712 geth
## 36        GASPRICE             5.699091                  0.44168670 geth
## 37     EXTCODESIZE            54.402727                  1.49153322 geth
## 38     EXTCODECOPY            75.333636                  3.08528679 geth
## 39  RETURNDATASIZE             4.389091                  0.42311061 geth
## 40  RETURNDATACOPY             6.158182                  4.85793288 geth
## 41     EXTCODEHASH            80.294545                  2.01307307 geth
## 42        COINBASE             7.583636                  0.25341669 geth
## 43       TIMESTAMP             4.667273                  0.49415909 geth
## 44          NUMBER             5.216364                  0.48731155 geth
## 45      DIFFICULTY             7.965455                  0.59710935 geth
## 46        GASLIMIT             5.489091                  0.71887551 geth
## 47         CHAINID             7.873636                  0.73076680 geth
## 48     SELFBALANCE            33.955455                  0.81140284 geth
## 49             POP             6.351818                  0.92773859 geth
## 50           MLOAD            13.090000                  2.58387312 geth
## 51          MSTORE            15.542727                  0.64385853 geth
## 52     MSTORE_COLD            20.066364                  0.79946671 geth
## 53         MSTORE8            15.713636                  1.26344868 geth
## 54            JUMP            13.087273                  1.56076857 geth
## 55           JUMPI            16.255455                  3.06719811 geth
## 56              PC             4.705455                  0.55076072 geth
## 57           MSIZE             4.151818                  0.57301828 geth
## 58             GAS             5.121818                  0.70241640 geth
## 59        JUMPDEST             4.398182                  0.42178030 geth
## 60           MCOPY            21.664545                  1.12249799 geth
## 61      MCOPY_COLD            26.647273                  1.09809820 geth
## 62           PUSH0             3.850000                  0.65833604 geth
## 63            LOG0           213.440909                  8.29884021 geth
## 64            LOG1           235.254545                  5.49659382 geth
## 65            LOG2           254.376364                  5.35003054 geth
## 66            LOG3           267.572727                  3.93156682 geth
## 67            LOG4           278.936364                  4.66722409 geth
## 68          CREATE          7374.918182                 29.02125572 geth
## 69            CALL           492.700909                  7.01310003 geth
## 70          RETURN            24.008182                  8.83634721 geth
## 71    DELEGATECALL           384.894545                  5.37142205 geth
## 72      STATICCALL           436.326364                  6.70211703 geth
## 73          REVERT            63.060000                  3.65110753 geth
## 74           PUSH1             5.988182                  0.15524825 geth
## 75           PUSH2             9.920909                  0.45168504 geth
## 76           PUSH3             9.985455                  0.62710904 geth
## 77           PUSH4             9.948182                  0.50785845 geth
## 78           PUSH5             9.115455                  0.23493175 geth
## 79           PUSH6            10.024545                  0.21998643 geth
## 80           PUSH7            10.144545                  0.18761443 geth
## 81           PUSH8            10.646364                  0.69364020 geth
## 82           PUSH9             9.098182                  0.56940483 geth
## 83          PUSH10            14.731818                  1.07020912 geth
## 84          PUSH11             9.487273                  0.16370481 geth
## 85          PUSH12             8.770000                  0.21612477 geth
## 86          PUSH13            10.134545                  0.24097598 geth
## 87          PUSH14             9.939091                  0.26840532 geth
## 88          PUSH15            10.447273                  0.32586059 geth
## 89          PUSH16             8.818182                  0.50234894 geth
## 90          PUSH17            10.014545                  0.66363941 geth
## 91          PUSH18            10.110909                  0.53693139 geth
## 92          PUSH19            10.787273                  0.21256318 geth
## 93          PUSH20             9.968182                  0.19637088 geth
## 94          PUSH21            10.198182                  0.31579131 geth
## 95          PUSH22            11.531818                  0.30135217 geth
## 96          PUSH23            11.699091                  0.34922175 geth
## 97          PUSH24            10.576364                  0.48727650 geth
## 98          PUSH25             9.968182                  0.27626244 geth
## 99          PUSH26            10.360000                  0.19707783 geth
## 100         PUSH27             9.917273                  0.22150645 geth
## 101         PUSH28            10.272727                  0.32293421 geth
## 102         PUSH29            10.369091                  0.28390301 geth
## 103         PUSH30            10.496364                  0.62398082 geth
## 104         PUSH31            11.196364                  0.20737637 geth
## 105         PUSH32            10.728182                  0.18510638 geth
## 106           DUP1             5.751818                  0.15266563 geth
## 107           DUP2             5.595455                  0.09854544 geth
## 108           DUP3             5.303636                  0.51241914 geth
## 109           DUP4             5.192727                  0.56996738 geth
## 110           DUP5             4.596364                  0.61965096 geth
## 111           DUP6             5.766364                  0.20685475 geth
## 112           DUP7             5.867273                  0.21660711 geth
## 113           DUP8             6.043636                  0.17465590 geth
## 114           DUP9             6.154545                  0.32172914 geth
## 115          DUP10             5.941818                  0.30320964 geth
## 116          DUP11             6.428182                  0.68779328 geth
## 117          DUP12             5.975455                  0.59110416 geth
## 118          DUP13             5.477273                  0.10260832 geth
## 119          DUP14             5.689091                  0.18698615 geth
## 120          DUP15             5.720000                  0.17719786 geth
## 121          DUP16             6.559091                  0.94966405 geth
## 122          SWAP1             6.424545                  0.50522622 geth
## 123          SWAP2             6.217273                  0.12882139 geth
## 124          SWAP3             6.181818                  0.14070529 geth
## 125          SWAP4             7.347273                  0.57817165 geth
## 126          SWAP5             5.833636                  0.15655585 geth
## 127          SWAP6             5.820909                  0.37275080 geth
## 128          SWAP7             6.112727                  0.92554027 geth
## 129          SWAP8             5.881818                  0.58024725 geth
## 130          SWAP9            13.953636                  2.07600748 geth
## 131         SWAP10             4.120909                  0.74979710 geth
## 132         SWAP11             5.113636                  1.07671724 geth
## 133         SWAP12             6.487273                  0.30956351 geth
## 134         SWAP13             7.237273                  0.82556595 geth
## 135         SWAP14             5.831818                  0.52400546 geth
## 136         SWAP15             6.023636                  0.11961984 geth
## 137         SWAP16             6.059091                  0.12227376 geth

The results are exported to reports-08.11.2024/estimated_cost_geth_full.csv.

if (params$output_estimated_cost != "") {
  write.csv(estimates, params$output_estimated_cost, quote=FALSE, row.names=FALSE)
}